RADICE：基于因果图的系统性能诊断根因分析
RADICE: Causal Graph Based Root Cause Analysis for System Performance Diagnostic
AI总结：
针对系统性能诊断中的根因分析，提出基于因果图的方法RADICE，利用部分领域知识构建因果模型，通过图发现等过程输出根因子图，实验优于基线方法。
摘要：
根因分析是软件可靠性中关于系统性能诊断的最关键操作之一。它旨在识别系统性能异常的根因，从而能够解决或预防可能导致数百万美元损失的问题。现有常见方法依赖于数据相关性或完全依赖领域专家知识，在大多数工业案例中不准确或不可行，因为相关性并不意味着因果关系，且领域专家可能无法完全了解复杂且实时的系统。在这项工作中，我们定义了一种新颖的因果领域知识模型，表示底层系统组件之间的因果关系，使领域专家能够为根因分析贡献部分领域知识。然后，我们介绍了RADICE，一种通过因果图发现、增强、细化和减法过程，能够输出根因因果子图的算法，该子图显示了受异常影响的系统组件之间的因果关系。我们使用模拟数据评估了RADICE，并报告了一个真实数据用例，分享了经验教训。实验表明，RADICE在根因分析方面提供了比其他基线方法（包括因果发现算法和基于相关性的方法）更好的结果。
英文摘要：
Root cause analysis is one of the most crucial operations in software reliability regarding system performance diagnostic. It aims to identify the root causes of system performance anomalies, allowing the resolution or the future prevention of issues that can cause millions of dollars in losses. Common existing approaches relying on data correlation or full domain expert knowledge are inaccurate or infeasible in most industrial cases, since correlation does not imply causation, and domain experts may not have full knowledge of complex and real-time systems. In this work, we define a novel causal domain knowledge model representing causal relations about the underlying system components to allow domain experts to contribute partial domain knowledge for root cause analysis. We then introduce RADICE, an algorithm that through the causal graph discovery, enhancement, refinement, and subtraction processes is able to output a root cause causal sub-graph showing the causal relations between the system components affected by the anomaly. We evaluated RADICE with simulated data and reported a real data use case, sharing the lessons we learned. The experiments show that RADICE provides better results than other baseline methods, including causal discovery algorithms and correlation based approaches for root cause analysis.
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